1,082 research outputs found
PU-Transformer: Point Cloud Upsampling Transformer
Given the rapid development of 3D scanners, point clouds are becoming popular
in AI-driven machines. However, point cloud data is inherently sparse and
irregular, causing major difficulties for machine perception. In this work, we
focus on the point cloud upsampling task that intends to generate dense
high-fidelity point clouds from sparse input data. Specifically, to activate
the transformer's strong capability in representing features, we develop a new
variant of a multi-head self-attention structure to enhance both point-wise and
channel-wise relations of the feature map. In addition, we leverage a
positional fusion block to comprehensively capture the local context of point
cloud data, providing more position-related information about the scattered
points. As the first transformer model introduced for point cloud upsampling,
we demonstrate the outstanding performance of our approach by comparing with
the state-of-the-art CNN-based methods on different benchmarks quantitatively
and qualitatively
Patch-based Progressive 3D Point Set Upsampling
We present a detail-driven deep neural network for point set upsampling. A
high-resolution point set is essential for point-based rendering and surface
reconstruction. Inspired by the recent success of neural image super-resolution
techniques, we progressively train a cascade of patch-based upsampling networks
on different levels of detail end-to-end. We propose a series of architectural
design contributions that lead to a substantial performance boost. The effect
of each technical contribution is demonstrated in an ablation study.
Qualitative and quantitative experiments show that our method significantly
outperforms the state-of-the-art learning-based and optimazation-based
approaches, both in terms of handling low-resolution inputs and revealing
high-fidelity details.Comment: accepted to cvpr2019, code available at https://github.com/yifita/P3
Frequency-Selective Geometry Upsampling of Point Clouds
The demand for high-resolution point clouds has increased throughout the last
years. However, capturing high-resolution point clouds is expensive and thus,
frequently replaced by upsampling of low-resolution data. Most state-of-the-art
methods are either restricted to a rastered grid, incorporate normal vectors,
or are trained for a single use case. We propose to use the frequency
selectivity principle, where a frequency model is estimated locally that
approximates the surface of the point cloud. Then, additional points are
inserted into the approximated surface. Our novel frequency-selective geometry
upsampling shows superior results in terms of subjective as well as objective
quality compared to state-of-the-art methods for scaling factors of 2 and 4. On
average, our proposed method shows a 4.4 times smaller point-to-point error
than the second best state-of-the-art PU-Net for a scale factor of 4.Comment: 5 pages, 3 figures, International Conference on Image Processing
(ICIP) 202
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